Research on Using Model Predictive Control for the Truck Dry Friction Clutch Control during the Starting-Up Process - Le, Van Nghia; Nguyen, Quoc Trieu; Dam, Hoang Phuc; Tran, Trong Dat; Duong, Ngoc Khanh (Smart Systems and Devices, 2025)
Automating friction clutch engagement is essential to improving vehicle dynamic and driving comfort. The study proposes a Model Predictive Control (MPC) strategy for automated clutch engagement in truck launching, evaluated through simulation. The powertrain models are developed for designing and evaluating the controller. The MPC algorithm calculates the required friction torque to be transmitted through the clutch by minimizing the deviation between actual and desired parameters. Key performance metrics, including longidutianal jerk, specific friction work, and the dynamic load factor, are used to assess the effectiveness of the proposed control strategy. To further evaluate the controller’s impact on ride comfort, longitudinal jerk is analyzed through a co-simulation approach using specialized software. Simulation results for a first-gear launch under moderate intensity conditions show that the specific friction work is 18.4 J/cm², the dynamic load factor is 1.8, and the longitudinal jerk is 16.84 m/s³. These results confirm that the proposed MPC-based clutch control strategy ensures smooth engagement, enhances driving comfort, and meets performance requirements.